Snowflake announced on November 18, 2025 that it has integrated NVIDIA’s CUDA‑X Data Science libraries into its platform, enabling customers to run GPU‑accelerated machine‑learning workloads directly inside Snowflake’s Container Runtime, Notebooks, and ML Jobs without modifying existing code.
The integration brings NVIDIA’s cuML and cuDF libraries—key components of the CUDA‑X suite—into Snowflake. Benchmarks released by Snowflake show that Random Forest training can be accelerated up to five times and HDBSCAN clustering up to 200 times on NVIDIA A10 GPUs compared with CPU execution. Other sources report even higher gains, such as 50‑fold speed‑ups for scikit‑learn algorithms and 175‑fold improvements for HDBSCAN, underscoring the substantial performance lift available to data‑science teams.
By adding GPU acceleration to its AI‑Data‑Cloud, Snowflake reduces the time and infrastructure required to develop and deploy machine‑learning models. Faster training translates into lower compute costs and shorter model‑development cycles, giving customers a tangible return on investment. The move also strengthens Snowflake’s competitive positioning against hyperscalers that already offer native GPU support, positioning the company as a more complete AI platform for enterprises that need high‑performance analytics across multiple clouds.
The partnership deepens Snowflake’s focus on AI‑enabled services. Christian Kleinerman, EVP of Product, said the integration “gives our customers a massive performance boost” by allowing them to harness accelerated computing with their existing Python workflows. Pat Lee, VP of Strategic Enterprise Partnerships at NVIDIA, added that the collaboration “eliminates complexity and dramatically speeds up AI development.” Together, the two companies are expanding the AI Data Cloud ecosystem and accelerating adoption of GPU‑based analytics.
Analysts have responded positively to the announcement. On November 17, 2025, Mizuho’s Gregg Moskowitz raised Snowflake’s price target to $285 from $260, while BTIG’s Gray Powell increased the target to $312 from $276 on November 12. The upgrades reflect confidence in Snowflake’s AI strategy and the expected impact of the CUDA‑X integration on future revenue growth.
Snowflake plans to extend the integration to additional NVIDIA libraries and use cases over the coming months, further broadening the range of GPU‑accelerated workloads that can run natively on the platform. The company’s roadmap signals a continued commitment to delivering high‑performance, AI‑centric services that differentiate it in a crowded cloud data market.
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